samugit83/redamon/llm-provider-integration
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Checking that the SKILL.md file is still in the repository.
Adding an LLM provider to RedAmon: the credential boundary (keys must never reach scan containers), prefix-routed model ids, and the provider registry. A misrouted model id or a leaked key are the two failures this guards. Trigger: adding or editing an LLM provider; editing parse_model_provider in agentic/orchestrator_helpers/llm_setup.py, the webapp provider-type registry, the user_llm_providers model, or an LLM call site that passes an API key; a non-chat provider type (TypeSafe Jev) or a pat…
Install
npx vskill@latest install samugit83/redamon/llm-provider-integrationSKILL.md
The same SKILL.md was published earlier as gabrielmoreira/agent-skills-mirror/llm-provider-integration.
When to Use
- Adding a new LLM provider (OpenAI-compatible or otherwise) the agent can use.
For a non-LLM external API tool, use agentic-tool-integration.
Critical Rules
- NEVER let a provider API key reach a scan container. Keys live in exactly
two places: Postgres user_llm_providers rows and, in transit, on the wire between webapp and agent. The recon / scan / MCP containers must never see them. Do not thread a provider key through recon settings or container env.
- NEVER add a model id that is not prefix-routed. Anything other than
claude-* and bare OpenAI ids MUST carry a provider/<model> prefix, resolved by parse_model_provider() at agentic/orchestrator_helpers/llm_setup.py:67. An unprefixed id routes to the wrong provider silently.
- NEVER treat every `user_llm_providers` row as a chat LLM. A non-chat type
(today jev, the TypeSafe decision API) must be skipped by every path that builds a chat model, or its token is sent to the wrong host: setup_llm's custom branch builds ChatOpenAI(api_key=<row key>) with no base URL, which is api.openai.com. Add the type to NON_CHAT_PROVIDER_TYPES in BOTH twins (agentic/llm_builder.py, webapp/src/lib/llmProviderKinds.ts); use chat_providers() / isChatProvider wherever rows are listed, counted, gated or forwarded (the /api/models forward, the project LLM gate, the triage preflight, the feature-model grid); and never let a stale custom/<id> fall back to "the first provider" (custom_provider_or_fallback falls back only to custom-capable types). setup_llm also refuses a non-chat type outright.
- A non-chat provider's endpoint is a constant, and its type is locked.
agentic/jev_client.py is the only code that talks to api.typesafe.ai: a fixed base URL, no redirects, fixed error texts. On the webapp, POST forces model, URL and headers and allows one row per user; PUT and the test route refuse a type change in either direction and ignore a body-supplied URL/headers for a stored row, so a stored token can never be re-pointed at another host.
- ALWAYS register the provider in the webapp provider-type registry and
propagate the key kwarg into every LLM call site. A provider registered but not propagated builds a client with no credentials. The guide enumerates all 11 integration points; touch each.
The two invariants
| Invariant | Where | Failure if broken | |
|---|---|---|---|
| Keys only in Postgres + webapp<->agent transit | webapp/prisma/schema.prisma user_llm_providers; agent settings fetch | key leaks into scan/MCP containers | |
| Model id prefix routing | parse_model_provider() llm_setup.py:67 | model routes to the wrong provider |
Commands
Resources
- docs/readmes/coding_agent_prompts/PROVIDER_INTEGRATION_GUIDELINES.md - the 11 integration points, decision tree, and model-id prefix table
- Related skill:
agentic-tool-integration
Security
- Security scanPassed, 100 of 100Pattern checks on the skill's files.
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Source
- SKILL.md in the repositoryCheckingskills/llm-provider-integration/SKILL.md
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